7 Questions You Must Answer To Implement AI Governance Successfully

The rapid adoption of generative AI by businesses has led to a number of issues. From hallucinations to bias, security issues to model injection, the list goes on and on. You can minimize the risk by implementing AI governance. AI governance helps you manage your artificial intelligence projects efficiently and place guardrails to prevent abuse.

Implementing AI governance can be tough but it will help you realize the full potential of generative AI technology without worrying about the cyberattacks and data breaches. Wondering how to get started on your AI governance? This article is for you. In this article, you will learn about seven questions you must answer to successfully implement AI governance.

7 Questions You Must Answer To Implement AI Governance Successfully

Here are seven questions you must ask to ace AI governance.

1. What Business Objectives You Want To Achieve From AI Implementation?

It all starts from the business goals. Tie your generative AI implementation to the business objectives and outcomes you want to achieve and it will help you create a winning strategy and execute it too. When you align AI projects with strategic goals, you can improve data quality and overcome shortcomings.

Steve Smith, global partner of strategic projects at Esker said, “Many companies skip the most important step when it comes to AI experimentation: identifying specific use cases for this technology.Instead of experimenting and deploying AI aimlessly, a common tactic resulting from the AI hype, stakeholders need to come up with a clear, actionable strategy.”

2. Which Compliance Guidelines and Regulations Should Your Employees Follow When Using AI?

Rahul Pradhan, Vice President of Product and Strategy at Couchbase summed it up brilliantly when he said, “Governance structures help ensure AI systems comply with regulations, avoiding legal and financial repercussions. Enterprises must also adhere to industry regulations and standards, including data protection laws such as GDPR and HIPAA. Proper governance ensures data privacy is maintained and comprehensive security measures are in place to protect against data breaches and cyber threats.”

3. Which AI Tools Do Your Employees Use?

Employees may use a variety of artificial intelligence tools depending on their specific roles and the company’s industry. Common tools include AI-powered analytics platforms like IBM Watson, predictive modeling software such as TensorFlow or generative AI tools like OpenAI’s ChatGPT for content generation and customer interaction.

You should also be aware of which tool is running on which hardware so you can optimize it. For instance, your employees might be using large language models running on a dedicated hosting server Each tool should be evaluated for both its effectiveness and compliance with corporate policies. For example, employees working in marketing might use artificial intelligence to automate campaign analytics, while engineers may use machine learning platforms to improve product development cycles.

However, companies must ensure that employees use approved artificial intelligence tools that meet internal security standards and don’t expose the organization to risks. Clear guidelines should be provided for which tools are allowed and there should be mechanisms in place to monitor and restrict the use of unauthorized artificial intelligence applications.

Additionally, regular updates should be provided to ensure employees are aware of new tools and technologies available for use in their daily tasks while being fully compliant with both internal and external regulations.

4. How Will Employees Use Your Company Data In AI Tools?

When employees use company data in artificial intelligence tools, it is critical that they follow strict protocols to ensure data security and integrity.This involves categorizing data based on its sensitivity and adhering to rules regarding what types of information can be fed into AI models.

Employees should only use non-sensitive, anonymized data unless otherwise authorized and any data used should be aligned with the company's data protection and privacy policies. Encryption and access controls must be enforced to ensure that only authorized personnel can input or retrieve company data within AI systems.

Furthermore, employees should be educated on the risks of using company data in external or cloud-based AI tools, which could lead to unintended data exposure or breaches. A data governance framework should be in place, dictating how data can be utilized in artificial intelligence including logging and auditing of any data fed into AI models. Regular reviews should be conducted to ensure compliance with internal policies and regulations, safeguarding the organization's data from improper use or malicious actors.

5. Is There a Mechanism To Validate The Results From Ai Tools?

Validating the results from artificial intelligence tools is crucial to ensuring their reliability and minimizing potential risks. Employees should use a combination of human oversight, cross-validation with alternative methods and benchmarking against historical data to confirm AI-generated outputs.

For example, if a generative AI tool creates business reports or customer insights, these results should be compared to past performance metrics or verified through expert analysis. Establishing a feedback loop where artificial intelligence outcomes are regularly tested and refined is essential for improving the accuracy and dependability of these tools.

In addition to internal validation processes, companies should implement automated validation mechanisms within the AI systems themselves. These could include model performance tracking, regular audits and stress testing under different scenarios to detect anomalies or biases.

Third-party audits or external validation frameworks may also be useful to ensure impartiality and compliance with industry standards. Employees should be trained to understand these validation techniques and apply them consistently to avoid over-reliance on AI tools without human intervention.

6. How Much Does Your Data Strategy Change To Incorporate Generative AI?

Incorporating generative AI into a company's data strategy requires significant changes, especially in how data is stored, processed, and utilized. Data governance policies may need to be updated to ensure that information used in AI models is accurate, up-to-date and aligned with ethical standards.

Additionally, companies might need to implement new tools and platforms to handle the increased demand for data processing power and storage because generative AI models typically require large datasets to operate effectively. Investments in AI-specific infrastructure such as graphic processing units, data centers,  Budget Vps server and cloud-based machine learning environments may also be necessary.

7. Where Can Employees Hone Their Generative AI Skills?

Constantly educate your employees about how to use generative AI effectively and safely. Encourage them to play around with new tools and tell them about the best practices. This will prepare them for the generative AI world ahead.

According to Shibu Nambiar, global business leader at Genpact,“True progress combines AI’s capabilities with human insight, unleashing technology’s full potential to serve human needs and ethical standards.By training employees to prioritize human-centric AI, we will create a future where technology empowers people, enhances creativity, and upholds our core values.”

Which questions do you ask and answer when implementing AI governance? Share it with us in the comments section below.